A Review: The Internet of Things Using Fog Computing

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 3 | Mar -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 711
A Review: The Internet of Things Using Fog Computing
Miss. Yugandhara A. Thakare1, Miss. Pranita P. Deshmukh2, Miss. Rupali A. Meshram3,
Miss. Komal R. Hole4, Miss. Rutuja A. Gulhane5, Miss. Nilaja A. Deshmukh6
1,2,3,4,5,6Assistant Professor, PRMIT&R, Badnera, Maharashtra
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Abstract: Fog computing is a research area that targets on
providing services and specifying customers needsinthespace
between “Ground” and “Cloud”. In the current cloud-based
Internet-of-Things (IoT) model, smartdevices(suchassensors,
smart phones) exchange information through the Internet
(routers and/or servers on cloud) to cooperate and provide
services to users, which could be citizens, smart home systems,
and industrial applications. Even though the cloud based IoT
model describes a uniform, concise, and scalable solution for
supporting IoT applications, the deployments of IoT
applications on cloud and are facing the challengesoriginated
from economic considerations, social concerns, technical
limitations, and administrative issues.
The Internet of Things expands its reachintovirtually
every domain, high-speed data processing, analytics and
shorter response times are becoming more necessary than
ever. Meeting theserequirementsissomewhatdifficult through
the current centralized, cloud-based model powering IoT
systems, but can be made possible through fog computing
brings computing resources and application services closer to
the edge, the most rational and efficient spot in the range
between the data source and the cloud.
Keywords:- IoT, Fog Computing, Cloud
1. INTRODUCTION
The Internet of Things(IoT) promisestomakemany
items with consumer electronic devices, home appliances,
medical devices, cameras, and all types of sensors part of the
Internet environment [1].This opens the access to
innovations that make possible new interactions among
things and humans, and enables the realization of smart
cities, infrastructures, and services that enhance the quality
of life. The researchers approximation that the IoT could
have an economic impact—including, for example, revenue
generated and operational savings—of $11 trillion per year,
which would represent about 11 percent of the world
economy;[2] and that users will deploy1trillionIoTdevices.
The Internet of Things, can be a person with a heart
monitor implant, a farm animal with a biochip transponder,
an automobile that has fixed sensors to aware the driver
when tire pressure is low or any other natural or man-made
object that can be assigned an IP address and provided with
the ability to transfer data over a network. IoT has evolved
from the convergence of wireless technologies, micro-
electromechanical systems (MEMS), micro services and the
internet. The meeting has helped split down the silo walls
between operational technologies (OT) and information
technology, allowing unstructured machine-generated
data to be analyzedforinsightsthatwill driveimprovements.
Fog computing extends the Cloud Computing model
to the perimeter of the network, thus enabling a new type of
applications and services.
The characteristics of the Fog are:
a) Low latency and location awareness
b) Wide-spread geographical distribution
c) Mobility
d) Very huge number of nodes
e) Prime role of wireless access,
f) Strong presence of streaming and real time applications
g) Heterogeneity.
In this paper we argue that theabovecharacteristics
make the Fog the appropriate platform for a number of
critical Internet of Things (IoT) services and applications,
namely, Connected Vehicle, Smart Grid , Smart Cities, and, in
general, Wireless Sensors and ActuatorsNetworks(WSANs).
2. WHY IoT NEEDS FOG COMPUTING
The Internet of Things (IoT) is one of the hottest
mega-trends in technology and for good reason , IoT deals
with all the components including Big Data Analytics, Cloud
Computing and Mobile Computing .
2.1 The Challenge
The IoT promises to bring the connectivity to an
earthly level, every home, vehicle,andworkplacewithsmart,
Internet-connected devices. Butasdependenceonour newly
connected devices increases along withthe benefitsanduses
of a growing technology, the consistencyofthegatewaysthat
make the IoT a functional reality must increase and make
uptime a near guarantee [3]. As every appliance light, door,
piece of clothing etc. The Internet of Things is poised to
apply major stresses to the current internet and data
center infrastructure. Gardner predicts that the IoT may
include 26 billion connected units by 2020.

Figure 2.1 Gardner
The popularcurrentapproachistocentralizecloud
data processing in a single site, resulting in minor costs
and burly application security. But with the sheer amount of
input data that will be received from globally distributed
sources, this central processing structure will require
backup. Also most endeavor data is short of to the cloud,
stored and analyzed, after which a decision is made and
action taken. But this system isn’t efficient, to make it
efficient, there is a require to process some data or some big
data in IoT case in a smart way, especiallyifit’ssensitivedata
and need quick action.
To illustratethe needforsmartdispensationofsome
kind of data, IDC estimates that the amount of data
analyzed on devices that are physically close to the
Internet of Things is approaching 40 percent, which
supports the urgent need for a different approach to this
need.
Figure 2.2 Cisco
2.2 The Solution
To deal with this challenge, Fog Computing is the
champion. Fog computing allows computing, decision-
making and action-taking to occur via IoT devices and only
pushes related data to the cloud, Cisco coined the term
“Fog computing “and gave a brilliant definition for Fog
Computing: “The fog extends the cloud to be earlier to the
things that produce and act on IoT data.Thesedevices,called
fog nodes, can be deployed anywhere with a network
connection: on a factory floor, on top of a power pole,
alongside a railway track, in a vehicle, or on an oil rig. Any
device with computing, storage, and network connectivity
can be a fog node. Examples include industrial controllers,
switches, routers, embedded servers, and video surveillance
cameras.”
Figure 2.3 Cisco
2.3 To understand Fog computing concept, the
following actions define fog computing
 Analyzes the mainly time-sensitive data at the
network edge, close to where it is generated as a
substitute of sending vast amountsofIoTdata tothe
cloud.
 Acts on IoT data in milliseconds, based on strategy.
 Sends preferred data to the cloud for chronological
analysis and longer-term storage.
2.4 Benefits of using Fog Computing
 Minimize latency
 Conserve network bandwidth
 deal with security concerns at all stage of the
network
 Operate reliably with quick decisions
 Collect and secure wide range of data
 Move data to the best place for processing
 Lower operating cost of using high computing
power only when needed and less bandwidth
 Better analysis and insights of local data
Keep in mind that fogs computing is not a
replacement ofcloudcomputingby anyevaluate,itworks
in combination with cloud computing, optimizing the use of
available resources. But it was the product of a need to

address two challenges, real-time process and action of
incoming data, and limitation of resources like bandwidth
and computing power, another factor helping fog computing
is the fact that it takes advantage of the distributed nature of
today’s virtualized IT assets. This improvement to the data-
path hierarchy is enabled by the increased compute
functionality that manufacturers are building into their edge
routers and switches.
3. THE FOG COMPUTING PLATFORM
3.1 Characterization of Fog Computing
Fog Computing is a highly virtualized platform that
provides compute,storage,andnetworkingservicesbetween
end devices and conventional CloudComputingData Centers,
normally, but not entirely located at the edge of network.
Figure 3.1 presents the idealized informationandcomputing
architecture sustaining the upcoming IoT applications, and
illustrates the role of Fog Computing. Figureout,storage, and
networking resources are the building blocks of both the
Cloud and the Fog. Edge of the Network", however, implies a
amount of individuality that create the Fog a non-trivial
extension of the Cloud.
Let us list them with pointers to motivating
examples.
 Edge location,alertness,andlowlatency.Theorigins
of the Fog can be traced to early proposals to support
endpoints with rich services at the edge of the network,
including applications with low latency requirements (e.g.
gaming, video streaming, and augmented reality)[10].
Figure 3.1 The Internet of Things and Fog Computing
 Geographical Distribution: In sharp contrast to the
more centralized Cloud, the services and applications
embattled by the Fog demand broadly scattered
deployments. The Fog, for instance, will play an activerole
in delivering high feature streaming to moving vehicles,
through proxies and contact points positioned along
highways and tracks [6].
 Large-scale sensor networks to monitor the setting
and the Smart Grid are other examples of essentially
distributed systems, requiring distributed computing and
storage assets.
 Very large number of nodes, as a consequenceofthe
wide geo-distribution, as evidenced in sensor networks in
common and the Smart Grid in exacting. Support for
mobility. It is essential for many Fog applications to
communicate directly with mobile devices, and therefore
 Support mobility techniques, such as the LISP
protocol1, that decouple host identity from location
identity, and require a distributed directory system.
 Real-time Interactions: Important Fog applications
engage real-time interactionsratherthan batchprocessing
prevalence of wireless right to use
 Heterogeneity: Fog nodes come in different form
factors, and will be deployed in a wide variety of
environments.
 Interoperability and federation
Seamless support of certain services (streaming is a good
example) requires the cooperation of different providers.
Hence, Fog mechanism must be able to interoperate, and
services must be federated diagonally domains.
 Support for on-line analytic and interplay with the
Cloud. The Fog is positioned to play a significantroleinthe
intake and processing of the data close to the cause. We
detailed in section 4 on the interplay between Fog and
Cloud regarding Big Data.
4. COPING WITH INTERNET OF THINGS
DATA
IoT environmentsgenerateunprecedentedamounts
of data that can be useful in many ways, particularly if
analyzed for insights. However, the data volume can
overwhelm today’s storage systems and analytics
applications. Cloud computing could help by offering on-
demand and scalable storage, as well as processing services
that can scale to IoT requirements. However, for health
monitoring, crisis response, and other latency receptive
applications, the delay caused by transferring data to the
cloud and back to the application is unacceptable[11].
The Internet of Things (IoT) could facilitate
innovations that improve the excellence of life, but it
generates unprecedented amounts of data that are difficult
for traditional systems, the cloud, and even edge computing
to handle. Fog computing is designed to overcome these
limitations.

Many more data sources could easily reach millionsoftuples
per second. To address these issues, edge computing [4]was
proposed to use computing resources near IoT sensors for
local storage and preliminary data processing. This would
decrease network congestion, as well as accelerate analysis
and the ensuing assessment production. However, edge
devices can’t handle multiple IoT applications competingfor
their limited resources, which results in resourcecontention
and increases processing latency[12].
Fog computing—which seamlessly integrates edge
devices and cloud resources—helps overcome these
limitations. It avoids resource contention at the edge by
leveraging cloud resources and coordinating the use of
geographically distributed edge devices.
4.1 How fog computing pushes IoT intelligence
to the edge
As the Internet of Things evolves into theInternetof
Everything and expands its get to into nearly every area,
high-speed data processing, analytics and shorter response
times are fetching more essentially than ever [5]. Meeting
these requirements is somewhat difficult through the
existing centralized, cloud-based representation powering
IoT systems, but can be made probable through fog
computing, a decentralized architectural blueprint
that brings computing resources and application services
closer to the edge, the most rational and well-organized spot
in the continuum between the data source and the cloud.
The phrase fog computing, coined by Cisco,refersto
require for bringing the compensation and power of cloud
computing closer to where the data is being generated and
acted upon. Fog computing reduces the quantity of data that
is transferred to the cloud for giving out and analysis, while
also improving security, a major concern in the IoT industry.
Here is how transitioning from the cloud to the fog can help
contract with the current and future challenges of the IoT
industry.
4.2 The Problem with the Cloud
The IoT owes its explosive growth totheassociation
of objective things and function technologies (OT) to
analytics and machine learning applications, which can help
glean insightsfromdevice-generateddata andenabledevices
to create “smart” decisions lacking human involvement.
Currently, such resourcesaremostlybeingprovidedbycloud
service providers, where the computation and storage
capacity exists [4].
However, in spite of its power, the cloud model is
not applicable to environments where operations are time-
critical or internet connectivity is deprived.Thisisespecially
true in scenarios such as telemedicine and patient concern,
where milliseconds can have serious consequences. The
same can be said about vehicle to vehicle communications,
where the prevention of collisionsandaccidentscan’t payfor
the latency caused by the roundtrip to the cloud server. The
cloud paradigm is like having your brain command your
limbs from miles away — it won’t help you where you
require sudden reflexes. besides, having every device
associated to the cloud and sending unprocessed data over
the internet can have privacy,securityandlegal implications,
especially when dealing with sensitive data that is subject to
separate regulations in different countries.
4.3 Does the fog eliminate the cloud
Fog computing improves efficiency and reduces the
amount of data that wants to be sent to the cloud for giving
out. But it’s here to complement the cloud, not replaceit. The
cloud will continue to have a pertinent role in the IoT cycle.
In fact, with fog computing shouldering the load of short-
range analytics at the perimeter, cloud resources will be
freed to take on the heavier tasks, especially where the
analysis of historical data and huge datasetsis apprehensive.
Insights obtained by the cloud can help update and tweak
policies and functionality at the fog layer [7, 8].
And there are still manycaseswherethecentralized,
highly proficient computing infrastructure of the cloud will
outperform decentralized systems in concert scalability and
expenses. This includes environments where data needs to
be analyzed from largely dispersed sources. It is the
combination of fog and cloud computing that will accelerate
the adoption of IoT, especially for the enterprise.
5. CONCLUSION
Moving the intelligent processing of data to the
perimeter only raises the stakes for maintaining the
accessibility of these smart gateways and their
communication path to the cloud. When the IoT provides
methods that allow public to deal with their everyday lives,
from locking their homes to examination their schedules to
cooking their meals, gateway downtimeinthefogcomputing
world becomes a critical issue. Additionally, flexibility and
failover solutions that maintain those processes will develop
into even more necessary. Fog computing enables the seam-
less integration of edge and cloud resources. It ropes the
decentralized and smart processing of exceptional data

volumes generated by IoT sensors deployed for smooth
combination of objective and cyber environments.
This could produce many settlement to society by,
for example, enabling smart healthcare applications. The
further development of fog computing could thus help the
IoT reach its vast potential.
REFERENCES:
[1]R. Buyya and A. Dastjerdi, Internet of Things: Principles
and Paradigms,Morgan Kaufmann, 2016
[2]. J. Manyika et al.Unlocking the Potential of the Internet of
Things, McKinsey & Company, 2015.
[3] G. Gan, Z. Lu, and J. Jiang, “Internet of Things Security
Analysis”, 2011 International Conference on Internet
Technology and Applications (iTAP), August 16-18, 2011
[4]J. Chong, C. Joe-Wong, S. Ha and M. Chiang, “CYRUS:
Toward client-defined Cloud storage,” Proceedings of
EuroSys 2015.
[5] F. Bonomi, R. Milito, P. Natarajan and J. Zhu, “Fog
computing: A Platform for Internet of Things and Analytics,”
in Big Data and Internet of Things: A Roadmap for Smart
Environments, Springer, March 2014, pp 169-186.
[6] S. Agarwal, J. Dunagan, N. Jain,S.Saroiu,A.Wolman,andH.
Bhogan. Volley: Automated data placement for geo-
distributed cloud services. In NSDI, 2010.
[7] I. Stojmenovic and S. Wen. The fog computing
paradigm:Scenarios andsecurityissues.InComputerScience
and Information Systems (FedCSIS), 2014 Federated
Conference on, 2014.
[8] Bonomi, Flavio, et al. "Fog computing and its role in the
internet of things." Proceedings of the first editionoftheMCC
workshop on Mobile cloud computing. ACM, 2012, pp. 13-
16).
[9] F. Bonomi, ―Connected vehicles, the internet of things,
and fog computing,‖ in The Eighth ACM International
Workshop on Vehicular Internet Networking (VANET), Las
Vegas, USA, 2011.
[10] Zhu, Jiang, ―Improving Web Sites Performance Using
Edge Servers in Fog Computing Architecture‖, Service
Oriented System Engineering (SOSE), IEEE. 2013.
[11] Amir Vahid Dastjerdi, Rajkumar Buyya – “Fog
Computing: Helping the Internet of Things Realize its
Potential.” IEEE, University of Melbourne , 2016.
[12] R. Cortés et al., “Stream Processing of HealthcareSensor
Data: Studying User Traces to Identify Challenges from a Big
Data Perspective,” Procedia Computer Science, vol.52,2015,
pp. 1004–1009.

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